TensorFlow Quickstart
TensorFlow Quickstart
What you’ll learn
Skills you’ll gain
Do you want to dive into machine learning? TensorFlow is an essential tool that enables the development of complex machine learning models efficiently. In this course, Greg Damico covers everything from installing TensorFlow and grasping its core concepts to implementing advanced techniques like neural networks and batch normalization. After this course, you'll have practical skills in constructing, training, and optimizing models for real-world applications with TensorFlow.
Syllabus
Download syllabus-
1
Get started with TensorFlow TensorFlow is a powerful tool for machine learning that enables developers to create complex models with ease. 1m
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2
Understand TensorFlow's core concepts Grasping the basic building blocks of TensorFlow is crucial for any machine learning project. 4m
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3
TensorFlow vs. other frameworks Understanding how TensorFlow compares to other machine learning frameworks can help you choose the right tool for your projects. 1m
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Define and manipulate tensors Tensors are the fundamental data structures in TensorFlow and understanding them is essential. 3m
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2
Utilize TensorFlow operations Operations are what make TensorFlow powerful for machine learning tasks. 2m
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3
Utilizing variables and placeholders Variables and placeholders are key to feeding data into your TensorFlow models. 3m
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Build your first machine learning model Building a machine learning model with TensorFlow is an exciting step. 2m
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2
Train and evaluate your model Training and evaluating are critical steps in the machine learning workflow. 3m
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3
Debug your models Debugging is key to improving your machine learning model's performance. 2m
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4
Save and restore models Learning how to save and restore TensorFlow models is crucial for long-term projects. 2m
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1
Implement a neural network Neural networks are at the heart of deep learning applications. 2m
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2
Explore advanced optimization techniques Advanced optimization techniques can significantly improve your model's training speed and performance. 3m
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3
Implement batch normalization Batch normalization is a technique to improve training stability and performance. 1m
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4
Use Dropout for regularization Dropout is a regularization technique to prevent overfitting in neural networks. 1m
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1
Start building with TensorFlow Thanks for watching! 1m
Certificate
Certificate of Completion
Awarded upon successful completion of the course.
Instructor
Greg Damico
Greg is an instructor with experience at both the collegiate and the tech bootcamp levels, and he is currently teaching at Bellevue College in the Seattle area. He is interested in pedagogy and much of his work revolves around bringing data science tools and concepts to a wider audience. Greg holds an MS in applied mathematics from the University of Washington and a Ph.D in philosophy from the University of California, Davis.
Greg Damico
Data Analyst, Researcher, and Instructor
Accreditations
Link to awards
How GoSkills helped Chris
I got the promotion largely because of the skills I could develop, thanks to the GoSkills courses I took. I set aside at least 30 minutes daily to invest in myself and my professional growth. Seeing how much this has helped me become a more efficient employee is a big motivation.
